Overview

Introduction


We explore the effect of COVID-19 pandemic on crime rates across different cities in the US to get a glimpse into the future of crime in these unprecedented circumstances.

Our goal is to use COVID and Crime data to make future predictions on crime rates in this pandemic. We also look at the crime rates before and after the pandemic to gain insight into how quarantine and self-isolation measures influence human behaviour leading to an increase in specific types of crime.

Click on the location markers on the left to view their analysis !

Key Results


Some text desccription

Coming Soon


Some text desccription

Chicago

Row

Chart 1

Chart 2

Chart 3

Row

Chicago Crime Summary

Assault

Battery

Criminal Damage

Deceptive Practice

###Theft

Row

Daily confirmed cases of COVID19 in Chicago

Summary

This is text link

  • one
  • two
  • three

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

Providence

Boston

Philadelphia

Los Angeles

Atlanta

Seattle

---
title: "COVID-19 and US Crime"
author: ""
output: 
  flexdashboard::flex_dashboard:
    theme: flatly
    orientation: rows
    social: menu
    source: embed
---

```{r setup, include=FALSE}
library(ggplot2)
library(plotly)
library(plyr)
library(flexdashboard)
library(RSocrata)
library(tidyverse)
library(tsbox) # transform data into time series
library(xts)
library(COVID19) # to get data about covid 19
library(aTSA) # adf.test
library(forecast) #ariRI model
library(vars) #VAR and Causality
library(dygraphs)
library(leaflet)
library(htmlwidgets)
#[INCOMPLETE] Graphs in Chicago have been converted here to Plotly HTML Widgets 
# Make some noisily increasing data [Testing Purposes]
set.seed(955)
dat <- data.frame(cond = rep(c("A", "B"), each=10),
                  xvar = 1:20 + rnorm(20,sd=3),
                  yvar = 1:20 + rnorm(20,sd=3))

#Load Chicago Data
covid19_CH <- covid19("USA", level = 3) %>%
  # this cook county contains chicago
  filter(administrative_area_level_3 == "Cook",
         administrative_area_level_2 == "Illinois" ) %>%
  # filter out days when confirmed is zero or one
  # becasue when it was 2 for a very long time
  filter(confirmed > 2)

```

Overview {.storyboard}
=======================================================================

### Introduction

```{r}

Location <- c("Providence ","Los Angeles","Chicago","Boston","Seattle","Atlanta","Philadelphia" )
lat <- c(41.8240,33.82377,41.78798,42.361145,47.714965,33.753746, 39.952583)
lng <- c( -71.4128,-118.2668,-87.7738,-71.057083,-122.127166 ,-84.386330,-75.165222)
df <- data.frame(Location, lat,lng)
 jsCode <- paste0('
 function(el, x) {
  var marker = document.getElementsByClassName("leaflet-marker-icon leaflet-zoom-animated leaflet-interactive");
  marker[0].onclick=function(){window.open("http://individual.utoronto.ca/nayan/datafest.html#providence");};
  marker[1].onclick=function(){window.open("http://individual.utoronto.ca/nayan/datafest.html#los-angeles");};
  marker[2].onclick=function(){window.open("http://individual.utoronto.ca/nayan/datafest.html#chicago");};
  marker[3].onclick=function(){window.open("http://individual.utoronto.ca/nayan/datafest.html#boston");};
  marker[4].onclick=function(){window.open("http://individual.utoronto.ca/nayan/datafest.html#seattle");};
  marker[5].onclick=function(){window.open("http://individual.utoronto.ca/nayan/datafest.html#atlanta");};
  marker[6].onclick=function(){window.open("http://individual.utoronto.ca/nayan/datafest.html#philadelphia");};
}
 ')
m <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(data=df,)%>%
   onRender(jsCode)
m  # Print the map
```

***

We explore the effect of COVID-19 pandemic on crime rates across different cities in the US to get a glimpse into the future of crime in these unprecedented circumstances. 

Our goal is to use COVID and Crime data to make future predictions on crime rates in this pandemic. We also look at the crime rates before and after the pandemic to gain insight into how quarantine and self-isolation measures influence human behaviour leading to an increase in specific types of crime.

Click on the location markers on the left to view their analysis !

### Key Results

```{r}
```

***

Some text desccription

### Coming Soon

```{r}
```

***

Some text desccription


  
Chicago
=======================================================================





Row{data-height=300 .tabset .tabset-fade }
-------------------------------------
   

### Chart 1
```{r, echo = FALSE}
#Overview of the data 
# plot cumulative cases
# extract for transforming into time series

ts_CH <- covid19_CH %>%
  dplyr::select(date, confirmed) %>%
  ts_xts()

ts_diff_CH <- na.omit(diff(ts_CH))

dygraph(ts_diff_CH,
         main = "Daily confirmed cases of
         COVID19 in Chicago")


covid19_CH_diff <- data.frame(diff(covid19_CH$confirmed))
colnames(covid19_CH_diff)[1] = "confirmed"
covid19_CH_diff$date = covid19_CH$date[2:length(covid19_CH$date)]
```

### Chart 2

```{r}

n <- 20
x1 <- rnorm(n); x2 <- rnorm(n)
y1 <- 2 * x1 + rnorm(n)
y2 <- 3 * x2 + (2 + rnorm(n))
A <- as.factor(rep(c(1, 2), each = n))
df <- data.frame(x = c(x1, x2), y = c(y1, y2), A = A)
fm <- lm(y ~ x + A, data = df)

p <- ggplot(data = cbind(df, pred = predict(fm)), aes(x = x, y = y, color = A))
p <- p + geom_point() + geom_line(aes(y = pred))
ggplotly(p)
```   

### Chart 3
    
```{r}

p <- ggplot(dat, aes(x=xvar, y=yvar)) +
            geom_point(shape=1) +    # Use hollow circles
            geom_smooth()            # Add a loess smoothed fit curve with confidence region
ggplotly(p)
```





Row {data-height=400 .tabset .tabset-fade }
-------------------------------------
   
### Chicago Crime Summary

```{r}
if (exists("chicago")) is.data.frame(get("chicago")) else chicago <- RSocrata::read.socrata(
  "https://data.cityofchicago.org/resource/ijzp-q8t2.csv?$where=year >= 2014",
  app_token = "hPU78MH7zKApdpUv4OVCInPOQ")


# add date
chicago <- chicago %>%
  mutate(Date = substr(date, start = 1, stop = 10)) %>%
  mutate(y_month  = substr(date, start = 1, stop = 7)) %>%
  mutate(month = substr(date, start = 6, stop = 7))

# summary of all crime
chicago_summary <- chicago %>%
  group_by(primary_type) %>%
  summarise(number_of_crime = n()) %>%
  arrange(desc(number_of_crime))

# looks life theft is seeing sharp drop

# year to year comparison
plt <- chicago %>%
  dplyr::select(y_month, month, primary_type, year) %>%
  filter(primary_type %in% chicago_summary$primary_type[1:5]) %>%
  count(year, month, primary_type) %>%
  na.omit()%>% ggplot(aes(x=month, y=n, group = year, color = as.character(year))) + geom_line() + facet_free(~primary_type,scales = "free", space = "free")+xlab("Month") +
           ylab("Cases") + theme(legend.title = element_blank())

ggplotly(plt)
```   
 
### Assault
    
```{r}
```

### Battery
    
```{r}
```

### Criminal Damage
    
```{r}
```

### Deceptive Practice
    
```{r}
```

###Theft
    
```{r}
```



Row {data-height=300} 
-----------------------------------------------------------------------





### Daily confirmed cases of COVID19 in Chicago

```{r}

# if (exists("chicago")) is.data.frame(get("chicago")) else chicago <- RSocrata::read.socrata(
#   "https://data.cityofchicago.org/resource/ijzp-q8t2.csv?$where=year >= 2014",
#   app_token = "hPU78MH7zKApdpUv4OVCInPOQ")
# 
# 
# # add date
# chicago <- chicago %>%
#   mutate(Date = substr(date, start = 1, stop = 10)) %>%
#   mutate(y_month  = substr(date, start = 1, stop = 7)) %>%
#   mutate(month = substr(date, start = 6, stop = 7))
# 
# # summary of all crime
# chicago_summary <- chicago %>%
#   group_by(primary_type) %>%
#   summarise(number_of_crime = n()) %>%
#   arrange(desc(number_of_crime))
# 
# # looks life theft is seeing sharp drop
# 
# # year to year comparison
# plt <- chicago %>%
#   dplyr::select(y_month, month, primary_type, year) %>%
#   filter(primary_type %in% chicago_summary$primary_type[1:5]) %>%
#   count(year, month, primary_type) %>%
#   na.omit()%>% ggplot(aes(x=month, y=n, group = year, color = as.character(year))) + geom_line() + facet_free(~primary_type,scales = "free", space = "free")+xlab("Month") +
#            ylab("Cases") + theme(legend.title = element_blank())
# 
# ggplotly(plt)

#TEST CHUNK UNCOMMENT TO REDUCE FILE RUN TIME [Design]
# n <- 20
# x1 <- rnorm(n); x2 <- rnorm(n)
# y1 <- 2 * x1 + rnorm(n)
# y2 <- 3 * x2 + (2 + rnorm(n))
# A <- as.factor(rep(c(1, 2), each = n))
# df <- data.frame(x = c(x1, x2), y = c(y1, y2), A = A)
# fm <- lm(y ~ x + A, data = df)
# 
# p <- ggplot(data = cbind(df, pred = predict(fm)), aes(x = x, y = y, color = A))
# p <- p + geom_point() + geom_line(aes(y = pred))
# ggplotly(p)

ts_CH <- covid19_CH %>%
  dplyr::select(date, confirmed) %>%
  ts_xts()

ts_diff_CH <- na.omit(diff(ts_CH))

dygraph(ts_diff_CH)%>%  dyOptions(fillGraph = TRUE, fillAlpha = 0.4, colors = "red")


covid19_CH_diff <- data.frame(diff(covid19_CH$confirmed))
colnames(covid19_CH_diff)[1] = "confirmed"
covid19_CH_diff$date = covid19_CH$date[2:length(covid19_CH$date)]
```


### Summary

This is text [link](http://www.example.com)

- one
- two 
- three

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

Providence
=======================================================================

Boston
=======================================================================

Philadelphia
=======================================================================

Los Angeles
=======================================================================

Atlanta
=======================================================================

Seattle
=======================================================================